code: def get_aggregated_data(base_dir, subdirs):
      """Aggregate dataframe for model and func benchmarks assumining directory is structured as
          DATA_PATH
          |_2020-02-20
              |_subdir1
                  |_func_benchmarks.csv
                  |_model_benchmarks.csv
                  |_func_benchmarks_cuda.csv (optional)
                  |_model_benchmarks_cuda.csv (optional)
              |_subdir2
                  ...
      Args:
          base_dir (pathlib.path): path containing month subdirectories
          subdirs (list): a list of all subdirectories to aggreagate dataframes from
      Returns: tuple of pd.DataFrames containing func and model benchmarks with dates
      """
      available_dates = get_available_dates(base_dir)
      func_df, model_df = pd.DataFrame(), pd.DataFrame()
  
      for subdir in subdirs:
          func_df_cpu, model_df_cpu = read_subdir(
              base_dir, available_dates, subdir
          )
          func_df_gpu, model_df_gpu = read_subdir(
              base_dir, available_dates, subdir, cuda=True
          )
          tmp_func_df = pd.concat([func_df_cpu, func_df_gpu])
          tmp_model_df = pd.concat([model_df_cpu, model_df_gpu])
  
          tmp_func_df["mode"] = subdir
          tmp_model_df["mode"] = subdir
  
          func_df = func_df.append(tmp_func_df)
          model_df = model_df.append(tmp_model_df)
  
      return func_df, model_df
  
file_path: CrypTen/benchmarks/dash_app/load_data.py
imports: import os
  import pandas
label_desc: Use image node_id 1 for calling a global function with example usage: get_aggregated_data(base_dir, subdirs) and returns: func_df,  model_df